Physics‐Grounded Probabilistic Bits for Hardware‐Efficient Intelligent Inference and Optimization

As intelligent systems increasingly rely on probabilistic inference and large‐scale optimization, deterministic hardware faces intrinsic limitations in efficiently exploring complex solution spaces. Here, we present a physics‐grounded probabilistic bit (p‐bit) that serves as a hardware‐native primitive for energy‐efficient intelligent inference and optimization. The proposed p‐bit exploits intrinsic stochastic electron capture in a multitrap ensemble at the Si–SiN _x interface, converting nanoscale defect dynamics into a controllable probabilistic output fully compatible with standard complementary metal–oxide–semiconductor technology. A width‐programmed gate‐pulse scheme enables robust and continuous probability modulation by controlling trap occupancy through pulse duration rather than voltage amplitude, improving scalability and tolerance to interconnect nonidealities. We develop a physics‐based analytical framework that quantitatively links multitrap capture kinetics to macroscopic drain‐current statistics and implement it as a SPICE (Simulation Program with Integrated Circuit Emphasis)‐compatible compact model, enabling direct cosimulation with conventional digital circuits. The resulting p‐bit exhibits a Boltzmann‐consistent sigmoid activation, supporting experimentally validated invertible logic and bidirectional probabilistic inference. Using experimentally calibrated characteristics, networks of these p‐bits solve a 50‐variable, 218‐clause 3‐satisfiability benchmark via controlled stochastic energy minimization, demonstrating system‐level relevance for intelligent optimization. This work establishes a scalable pathway toward hardware‐efficient probabilistic computing architectures for intelligent systems.